A human can view an image and effortlessly distinguish a face from the background even when the image is of poor quality. Providing this same capability to a computer requires more effort. Distinguishing objects in an image is called pattern recognition and comprises three major steps: isolation; extraction; and classification. The first step, isolation, segments each object in the image. Extraction measures a set of features, such as size or color that characterizes each object. Lastly, classification assigns each object to a class based on the set of measured features of the object. Castleman, Digital Image Processing, pp. 447–546, Prentice-Hall, (1996) describes each of the steps and is herein incorporated by reference.
Thresholding is one method of segmenting an image and has the advantage of being computationally simple. The pixel value of each pixel in the image is compared against a threshold value and assigned a new pixel value depending on whether the original pixel value is greater than or less than the threshold value. Thresholding works well when the object, or target, of interest has a substantially uniform gray level that is significantly different from the gray level of the background.
A common problem in automated image processing systems is that the threshold value required to properly segment the image depends on the quality of the images being processed. Adaptive threshold systems adjust the threshold value according to the image characteristics, but require more computational resources that may make the application cost prohibitive. Alternatively, if the samples are fairly uniform, such as PC boards, and the lighting conditions during image capture are tightly controlled, the threshold value may be set once at the beginning of the automated inspection process.
FIG. 1 is a schematic of an automated scanning optical microscopy system. The automated scanning optical microscopy system 100 includes an optical microscope modified to automatically capture and save images of a sample 105 placed on a sample holder 107 such as, for example, a slide, which in turn is supported by a stage 110. The optical components include an illumination source 120, objective lens 124, and camera 128. Housing 130 supports the optical components. The design and selection of the optical components and housing are known to one of skill in the optical art and do not require further description.
The automated system 100 includes a controller that enables the stage 110 supporting the slide 107 to move a portion of the sample 105 into the focal plane of the objective lens and to translate the stage within the focal plane of the objective lens to allow different portions of the sample to be viewed and captured. The camera 128 captures an image of the sample and sends the image signal to an image processor for further processing and/or storage. In the example, shown in FIG. 1, the image processor and controller are both housed in a single PC 104 although other variations may be used. The mechanical design of the stage 110 is known to one of skill in the mechanical arts and does not require further description.
The controller may also control a sample handling subsystem 160 that automatically transfers a slide 109 between the stage 110 and a storage unit 162. The prepared sample slides are loaded into the storage unit 162 and the storage unit 162 is loaded on the sample handling subsystem 160. The loading of the slides into the storage unit or the loading of the storage unit into the handling subsystem may be done manually by an operator or may be automated. After the handling subsystem is loaded, the operator may enter information describing or identifying the samples into the processor. The operator may also enter or select parameters that govern how the scanning microscopy system will operate during the automated run. For example, the operator may choose to process all of the loaded sample slides in one continuous run or choose to terminate the run after a selected number of slides have been processed. As a further example, the operator may view one or more images captured from the samples and set threshold values such as the ones described below. After the run parameters are entered, the operator starts the run and the processor takes control of the system until the run is completed or terminated by the controller.
The image captured by the camera 128 may be preprocessed before being stored or sent to the image processor. The hardware and basic software components for the capture, storage, retrieval, display, and manipulation of the image are known to one of skill in the art and are not further discussed. The image processor may correct for camera artifacts, enhance particular objects of the image to simplify the object recognition process, or adjust or compensate for the lighting conditions used to capture the image.
In many situations, however, the properties of the sample itself produce images where the pixel values (gray levels) of the background do not differ significantly from the pixel values of the target. For example, epifluorescence microscopy of biological samples usually produces low light signal images because of the low signal strength of the fluorophore used to tag the biological samples. Under low light conditions, the average pixel value of the image is close to zero. A similar situation occurs under low contrast conditions where the difference between the average pixel value of the target and the average pixel value of the background is close to zero. In both conditions, closeness is relative to the maximum pixel value. For example, if the pixel depth is eight bits, the maximum pixel value is 255 and a pixel difference of 16 may be considered close. Similarly, if the pixel depth is 16 bits, the maximum pixel value is 65,535 and a pixel difference of 512 may be considered close. If the threshold is set to the average pixel value when the average value is close to zero, the segmentation will be susceptible to false positives due to background noise.
Therefore, there remains a need for a method of image segmentation that may be used in automated image processing systems that is capable of handling low light/low contrast images.